Moving object detection system and method

ABSTRACT

Disclosures of the present invention describe a moving object detection system and method, wherein a pre-processer module, a feature extraction module, an image optical flow estimation module, a feature points grouping module, and a moving object determination module are provided in a controlling and processing module of the system by a form of library, variables, or operands. Moreover, a feature difference calculation unit, a matrix establishing unit and a corner feature point acquiring unit are provided in the feature extraction module, and that is helpful for enhancing computing speed of the controlling and processing device in verifying corner feature points from image frames. Therefore, after the corner feature points are applied with a cluster labeling process, the moving object determination module can achieve motion detection of at least one object locating in a monitoring area by determining whether corner feature point groups move or not.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to the technology field of imageprocessing, and more particularly to a moving object detection systemand method capable of being implemented into unmanned aerial vehicle(UAV), satellite, robotics system for space exploration, andsurveillance system.

2. Description of the Prior Art

In traditional surveillance system, occurrences of abnormal events canbe detected by analyzing trajectories and behaviors of the objectsmoving in a monitoring area. Therefore, moving object detectiontechnology has been known to play an important role in an automaticsurveillance system. For instance, U.S. Pat. No. 6,867,799 discloses anapparatus for object surveillance with a movable camera. The disclosedobject surveillance apparatus comprises: a moving object detection unit,a selector unit, a translator unit, a coordinating future position unit,and a movement coordinator. From the disclosures of U.S. Pat. No.6,867,799, it is understood that, the object surveillance apparatus isconfigured for holding an object of interest in a field of view of amovable video camera, wherein the object of interest is selected from aplurality of moving objects detected in the field of view. Moreover, anindication of the selected moving object is received and is used topredict a future position thereof, such that movement commands for thecamera are created based on the future position of the moving object, soas to make the moving object be remained in the field of view of thecamera.

In the aforementioned and other moving object detection methods, imagealignment and visual optical flow tracing are two of the most commonmoving object detection techniques for movable platforms. FIG. 1 shows aflowchart of a conventional moving object detection method using imagealignment technique. In the moving object detection method shown in FIG.1, steps S1′ and S2′ are firstly executed for respectively acquiring aprevious-time image frame and a current-time image frame. The saidprevious-time image frame and the said current-time image frame can berespectively an image frame captured at time point “t−1” and an imageframe captured at time point “t”, and can also be respectively an imageframe captured at time point “t” and an image frame captured at timepoint “t+1”. The method flow subsequently proceeds to step S3′, so as toapply an image alignment process to the two image frames. Subsequently,in step S4′, the two image frames are applied with an image subtractionprocess so as to find out difference features between the two imageframes. Consequently, the method flow is proceeded to step S5′, suchthat a controller or a microprocessor is able to achieve thedetermination of at least one moving object from the two image framesbased on the difference features. Computer engineers skilled inconventional moving object detection method using image alignmenttechnique should know that, the moving object detection method usingimage alignment technique has advantages of merely using single camerato capture images and high image processing speed. However, it needs toparticularly note that, the image alignment technology used for findingout the difference features from the two image frames is a contour-basedapproach. In general, the region-based approach is more accurate andnoise-resistant than the contour-based approach for image alignmentunder the same illumination conditions.

On the other hand, FIG. 2 illustrates a flowchart of anotherconventional moving object detection method using visual optical flowtracing technique. In the moving object detection method shown in FIG.2, step S1 a is firstly executed for acquiring a previous-time imageframe and a current-time image frame. Next, step S2 a is executed forgenerating corresponding image depth maps according to the two imageframes, and step S3 a is simultaneously executed for calculating aprecise optical flow between the two image frames. Next, in step 4 a, abackground optical flow trajectory is estimated based on the image depthmaps and the optical flow data. As a result, by subtracting thebackground optical flow with the image optical flow (step S5 a),difference features are therefore obtained for use in the detection ofat least one moving object (step S6 a). Computer engineers skilled inconventional moving object detection method using visual optical flowtracing technique should know that, the moving object detection methodusing visual optical flow tracing technique has advantages of being ableto precisely track the moving object and obviously enhancing theaccuracy of the moving object determination. However, it is a pity that,there are at least two cameras used in the visual optical flow tracingtechnology for carrying out the subtraction process of the backgroundoptical flow and the image optical flow. Such specific way does not onlylead the whole apparatus cost to be more expensive, but also causes theimage processing speed become slower.

From above descriptions, it is known that both the image alignmenttechnology and the optical flow tracing technology show drawbacks andshortcomings in their practical applications of moving object detection.In view of that, inventors of the present application have made greatefforts to make inventive research thereon and eventually provided amoving object detection system and method.

SUMMARY OF THE INVENTION

The primary objective of the present invention is to provide a movingobject detection system and method, wherein the moving object detectionsystem mainly comprises an image capturing device and a controlling andprocessing module. Moreover, a pre-processer module, a featureextraction module, an image optical flow estimation module, a featurepoints grouping module, and a moving object determination module areprovided in the controlling and processing module by a form ofapplication program, library, variables, or operands. In the presentinvention, more particularly, a feature difference calculation unit, amatrix establishing unit and a corner feature point acquiring unit arefurther provided in the feature extraction module, and that is helpfulfor largely enhancing the computing speed of the controlling andprocessing device in verifying and choosing corner feature points fromthe image frames. Therefore, after the corner feature points are appliedwith a cluster labeling process, the moving object determination modulecan achieve motion detection of at least one object locating in amonitoring area by just determining whether corner feature point groupsmove or not.

In order to achieve the primary objective of the present invention, theinventor of the present invention provides an embodiment for the movingobject detection system, comprising:

-   an image capturing device, being connected to a camera carrying    device, and is used for applying an image capturing process to a    monitoring area having at least one object; and-   a controlling and processing module, comprising:    -   a pre-processer module, being configured for receiving a        plurality of image frames with time consecutiveness from the        image capturing device 11, so as to apply a background        subtraction process to the plurality of image frames; wherein        the plurality of image frames comprises a plurality of image        frame pairs, and each of the plurality of image frame pairs        consisting of a previous-time image frame and a current-time        image frame;    -   a feature extraction module, being coupled to the pre-processer        module for applying a feature extraction process to the        plurality of image frames after the background subtraction        process is completed, so as to obtain a plurality of corner        feature points from each of the plurality of image frames;    -   an image optical flow estimation module, being coupled to the        feature extraction module, and being configured for establishing        a plurality of image pyramid pairs based on the plurality of        image frame pairs after the feature extraction process is        completed, so as to apply an optical flow calculation process to        the plurality of corner feature points contained in each of the        plurality of image pyramid pairs;    -   a feature points grouping module, being coupled to the image        optical flow estimation module, and being configured to apply a        cluster labeling process to the plurality of corner feature        points contained in each of the plurality of image pyramid        pairs, so as to obtain a plurality of corner feature point        groups; and    -   a moving object determination module, being coupled to the        feature points grouping module, and being configured to achieve        a motion detection of the at least one object by determining        whether the plurality of corner feature point groups move or        not.

Moreover, for achieving the primary objective of the present invention,the inventor of the present invention also provides an embodiment forthe moving object detection method, comprising following steps:

-   (1) providing an image capturing device for applying an image    capturing process to a monitoring area having at least one object;-   (2) providing a controlling and processing module comprising a    pre-processer module, a feature extraction module, an image optical    flow estimation module, a feature points grouping module, and a    moving object determination module, so as to use the pre-processer    module of the controlling and processing module to apply a    background subtraction process to a plurality of image frames    received from the image capturing device; wherein the plurality of    image frames comprises a plurality of image frame pairs, and each of    the plurality of image frame pairs consisting of a previous-time    image frame and a current-time image frame;-   (3) applying a feature extraction process, by the feature extraction    module, to the plurality of image frames after the background    subtraction process is completed, so as to obtain a plurality of    corner feature points from each of the plurality of image frames;-   (4) using the image optical flow estimation module to establish a    plurality of image pyramid pairs based on the plurality of image    frame pairs after the feature extraction process is completed;    wherein each of the plurality of image pyramid pairs comprises a    previous-time image pyramid and a current-time image pyramid;-   (5) applying an optical flow calculation process to the plurality of    corner feature points contained in each of the plurality of image    pyramid pairs by the image optical flow estimation module;-   (6) applying a cluster labeling process, by the feature points    grouping module, to the plurality of corner feature points contained    in each of the plurality of image pyramid pairs, so as to obtain a    plurality of corner feature point groups; and-   (7) the moving object determination module being configured to    achieve a motion detection of the at least one object by determining    whether the plurality of corner feature point groups move or not.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention as well as a preferred mode of use and advantages thereofwill be best understood by referring to the following detaileddescription of an illustrative embodiment in conjunction with theaccompanying drawings, wherein:

FIG. 1 shows a flowchart of a conventional moving object detectionmethod using image alignment technique;

FIG. 2 shows a flowchart of another conventional moving object detectionmethod using visual optical flow tracing technique;

FIG. 3 shows a framework diagram of a moving object detection systemaccording to the present invention;

FIG. 4 shows a functional block diagram of a controlling and processingdevice;

FIG. 5 shows a functional block diagram of a feature extraction module;

FIG. 6 shows a schematic diagram of one image pyramid pair;

FIG. 7A shows a first schematic diagram of an exemplary application ofthe moving object detection system;

FIG. 7B shows a second schematic diagram of an exemplary application ofthe moving object detection system;

FIG. 7C shows a third schematic diagram of an exemplary application ofthe moving object detection system;

FIG. 7D shows a fourth schematic diagram of an exemplary application ofthe moving object detection system;

FIG. 8A and FIG. 8B show flowcharts of a moving object detection methodaccording to the present invention; and

FIG. 9 shows a detail flowchart of step S3.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

To more clearly describe a moving object detection system and methodaccording to the present invention, embodiments of the present inventionwill be described in detail with reference to the attached drawingshereinafter.

FIG. 3 shows a framework diagram of a moving object detection systemaccording to the present invention. As FIG. 3 shows, the moving objectdetection system 1 of the present invention mainly comprises an imagecapturing device 11 and a controlling and processing module 12. Theimage capturing device 11 is connected to a camera carrying device 2,and is used for applying an image capturing process to a monitoring areahaving at least one object 3. On the other hand, the controlling andprocessing module 12 is configured for receiving a plurality of imageframes with time consecutiveness from the image capturing device 11, andthen achieving a motion detection of the at least one object 3 aftercompleting image processes of the plurality of image frames. FIG. 4shows a functional block diagram of the controlling and processingdevice. From FIG. 4, it is understood that the controlling andprocessing device 12 is particularly designed to comprise apre-processer module 121, a feature extraction module 122, an imageoptical flow estimation module 123, a feature points grouping module124, and a moving object determination module 125. Computer engineersskilled in moving object detection technologies should know that, eachof the pre-processer module 121, the feature extraction module 122, theimage optical flow estimation module 123, the feature points groupingmodule 124, and the moving object determination module 125 can beprovided in the controlling and processing module 12 by a form ofapplication program, library, variables, or operands.

In procedure of completing image processes of the plurality of imageframes, the pre-processer module 121 of the controlling and processingmodule 12 is firstly adopted for applying a background subtractionprocess to the plurality of image frames received from the imagecapturing device 11. It is worth noting that, the plurality of imageframes comprises a plurality of image frame pairs, and each of theplurality of image frame pairs consists of a previous-time image frameand a current-time image frame. The previous-time image frame and thesaid current-time image frame can be respectively an image framecaptured at time point “t−1” and an image frame captured at time point“t”, and can also be respectively an image frame captured at time point“t” and an image frame captured at time point “t+1”. Therefore, thefeature extraction module 122 in the controlling and processing device12 is subsequently configured for apply a feature extraction process tothe plurality of image frames after the background subtraction processis completed. As a result, a plurality of corner feature points areobtained from each the image frame.

Referring to FIG. 3 and FIG. 4 again, and please simultaneously refer toFIG. 5 showing a functional block diagram of a feature extractionmodule. FIG. 5 depicts that the feature extraction module 122 comprisesa feature difference calculation unit 1221, a matrix establishing unit1222 and a corner feature point acquiring unit 1223. To find out thecorner feature points from the plurality of image frames, the featureextraction module 122 firstly uses the feature difference calculationunit 1221 to apply a patch-based feature difference calculating processto the plurality of image frames. Particularly, the feature differencecalculation unit 1221 is provided with a feature difference calculationalgorithm therein for achieving the patch-based feature differencecalculating process, wherein the feature difference calculationalgorithm can be mean absolute differences (MAD) algorithm, sum ofabsolute differences (SAD) algorithm, sum of squared differences (SSD)algorithm, or scale-invariant feature transform (SIFT) algorithm. Forinstance, the SAD algorithm, presented by following mathematic equation(1), is embedded in the feature difference calculation unit 1221.S(x,y)=Σ_(u)Σ_(v) w(u,v)(I(u+x,v+y)−I(u,v))²  (1)

Subsequently, the matrix establishing unit 1222 provided in the featureextraction module 122 uses following mathematic equations (2)-(3) toestablish a plurality of structure tensor matrices based on a pluralityof feature difference calculation data provided by the featuredifference calculation unit 1221.

$\begin{matrix}{{S\left( {x,y} \right)} = {\left\lbrack {x\mspace{14mu} y} \right\rbrack{A\begin{bmatrix}x \\y\end{bmatrix}}}} & (2) \\{A = {\sum\limits_{u}{\sum\limits_{v}{{w\left( {u,v} \right)}\begin{bmatrix}{I_{x}\left( {u,v} \right)}^{2} & {{I_{x}\left( {u,v} \right)}{I_{y}\left( {u,v} \right)}} \\{{I_{x}\left( {u,v} \right)}{I_{y}\left( {u,v} \right)}} & {I_{y}\left( {u,v} \right)}^{2}\end{bmatrix}}}}} & (3) \\{R = {{\det(A)} - {k \times {{trace}(A)}}}} & (4)\end{matrix}$

In the eventual stage of the feature extraction process, a cornerfeature point acquiring unit 1223 in the feature extraction module 122is adopted to apply a feature value converting process to the pluralityof structure tensor matrices (A). After completing the feature valueconverting process, following mathematic equation (4) is subsequentlyadopted for calculating corresponding R values of each of the featurepoints. Next, specific corner feature points verifying approach forclosing the corner feature points is designed as the content showing infollowing Table (1). In addition, variables, parameters and/or notationsused in above-presented mathematic equations (1)-(4) are summarized andlisted in following Table (2).

TABLE 1 Verification approach Feature point verification result R issmall Feature point in flat region R is greater than a pre-definedFeature point in edge threshold value R is greater than a pre-definedFeature point in corner threshold value, and is a local maximum value inthe patch

TABLE 2 Variable, parameter or notation Description S(x, y) Sum ofabsolute differences (SAD) I(u, v) Feature point (pixel value) I(u + x,v + y) Feature point (pixel value) has moved by (x, y) in the patch w(u, v) Weight value for the feature point (pixel value) A Structuretensor matrix det(A) Determinant of A trace(A) Trace of A I_(x)(u, v)Partial derivative of feature point (pixel) in patch with respect to xcoordinate I_(y)(u, v) Partial derivative of feature point (pixel) inpatch with respect to y coordinate R Calculation result (or value) ofmathematic equation (4) k Tunable sensitivity parameter

After acquiring the plurality of corner feature points, the imageoptical flow estimation module 123 in the controlling and processingmodule 12 is subsequently utilized to establish a plurality of imagepyramid pairs based on the plurality of image frame pairs after thefeature extraction process is completed, and then apply an optical flowcalculation process to the plurality of corner feature points containedin each of the plurality of image pyramid pairs. FIG. 6 shows aschematic diagram of one image pyramid pair. According to the particulardesign of the present invention, the image optical flow estimationmodule 123 comprises an image pyramid establishing unit 1231 and afeature tracking unit 1232, wherein the image pyramid establishing unit1231 is configured for applying at least two times of Gaussianconvolution processes to each of the plurality of image frame pairs forgenerating the plurality of image pyramid pairs.

As FIG. 6 shows, the previous-time image is treated with two-times ofGaussian upsampling convolution processes, such that three levels ofimages constitute a previous-time image pyramid. Moreover, thecurrent-time image is applied with two-times of Gaussian downsamplingconvolution processes in order to generate a current-time image pyramidformed by three levels of images. Therefore, the feature tracking unit1232 in the image optical flow estimation module 123 is adopted forusing Lucas-Kanade (LK) optical flow approach to complete the opticalflow calculation process of the previous-time image pyramid and thecurrent-time image pyramid. However, owing to the fact that thecalculated optical flow value of each of the plurality of corner featurepoints may be nonlinear, linear interpolation should be utilized forachieving a liner correction on the optical flow value of each of theplurality of corner feature points contained in the image pyramid pairs.

After the optical flow calculation process is completed, the featurepoints grouping module 124 in the controlling and processing device 12is used for applying a cluster labeling process to the plurality ofcorner feature points contained in each of the plurality of imagepyramid pairs, so as to obtain a plurality of corner feature pointgroups. For instance, a feature point (pixel value) in a patch would beverified as a corner feature point P in the case of the fact that R isgreater than a pre-defined threshold value and is also a local maximumvalue in the patch. Subsequently, the feature points grouping module 124would determine whether the number of feature points in a pre-definedradius from the corner feature point P is smaller than a minimumthreshold value or not. If yes, those feature points are clustered to anidentical corner feature point group by the feature points groupingmodule 124. After the plurality of corner feature point groups areobtained, the moving object determination module 125 is configured toachieve a motion detection of the at least one object 3 by determiningwhether the plurality of corner feature point groups move or not.

In the present invention, the moving object determination module 125 isprovided with a motion vector calculation algorithm therein forachieving the motion detection of the at least one object 3, and themotion vector calculation algorithm is selected from the groupconsisting of least squares estimation algorithm, minimum least squaresestimation algorithm, and damped least square estimation algorithm. Forinstance, the minimum least squares estimation algorithm, presented byfollowing two mathematic equations, is embedded in the moving objectdetermination module 125. In addition, variables, parameters and/ornotations used in the following two mathematic equations are summarizedand listed in following Table (3).

${{{I_{x}\left( {x,y,t} \right)}V_{x}} + {{I_{y}\left( {x,y,t} \right)}V_{y}} + {I_{t}\left( {x,y,t} \right)}} = {{0\begin{bmatrix}V_{x} \\V_{y}\end{bmatrix}} = {\begin{bmatrix}{\sum\limits_{i = 1}^{n}{I_{x}\left( q_{i} \right)}^{2}} & {\sum\limits_{i = 1}^{n}{{I_{x}\left( q_{i} \right)}\mspace{14mu}{I_{y}\left( q_{i} \right)}}} \\{\sum\limits_{i = 1}^{n}{{I_{y}\left( q_{i} \right)}\mspace{14mu}{I_{x}\left( q_{i} \right)}}} & {\sum\limits_{i = 1}^{n}{I_{y}\left( q_{i} \right)}^{2}}\end{bmatrix}\begin{bmatrix}{- {\sum\limits_{i = 1}^{n}{{I_{x}\left( q_{i} \right)}\mspace{14mu}{I_{t}\left( q_{i} \right)}}}} \\{- {\sum\limits_{i = 1}^{n}{{I_{y}\left( q_{i} \right)}\mspace{14mu}{I_{t}\left( q_{i} \right)}}}}\end{bmatrix}}}$

TABLE 3 Variable, parameter or notation Description I_(x)(q_(i)) =I_(x)(x_(i), y_(i), t) Partial derivative of corner feature point (pixelvalue) with respect to x I_(y)(q_(i)) = I_(y)(x_(i), y_(i), t) Partialderivative of corner feature point (pixel value) with respect to yI_(t)(q_(i)) = I_(t)(x_(i), y_(i), t) Partial derivative of cornerfeature point (pixel value) with respect to t V_(x) Motion vector withrespect to x coordinate V_(y) Motion vector with respect to y coordinaten Positive integer

Following on from the previous descriptions, the moving objectdetermination module 125 is able to detect that the at least one cornerfeature point group is moving in the case of any one of the motionvectors being calculated to be not equal to zero. Briefly speaking, themoving object determination module 125 can achieve the motion detectionof the at least one object 3 by just determining whether the cornerfeature point groups move or not. On the other hand, the controlling andprocessing device 12 is further provided with a data storage module 126for storing the plurality of image frames, the plurality of image framesafter the background subtraction process is completed, the plurality ofimage frames after the feature extraction process is completed, theplurality of corner feature points contained in each of the plurality ofimage pyramid pairs, and the plurality of corner feature point groups.It is worth particularly explaining that, the adding of the featuredifference calculation unit 1221, the matrix establishing unit 1222 andthe corner feature point acquiring unit 1223 in the feature extractionmodule 122 is helpful for largely enhancing the computing speed of thecontrolling and processing device 12 in verifying and choosing cornerfeature points from the image pyramid pairs.

In spite of the fact that FIG. 3 depicts this moving object detectionsystem 1 is implemented into an aerial photography drone, but that doesnot used for forming a practical application limitation of the movingobject detection system 1. FIG. 7A, FIG. 7B, FIG. 7C, and FIG. 7D showvarious schematic diagrams of exemplary applications of the movingobject detection system. From the illustrations shown in FIG. 7A, FIG.7B, and FIG. 7C it is understood that this moving object detectionsystem 1 can also be applied in an unmanned aerial vehicle (UAV), asatellite, or a robotics system for space exploration. Of course, themoving object detection system 1 can also be applied in current-popularrobot cleaners. On the other hand, FIG. 7D depicts that the imagecapturing device 11 of the moving object detection system 1 can be asurveillance camera.

It needs to emphasize that, although FIG. 3 and FIG. 4 indicate that themoving object detection system 1 of the present invention seems having ahardware framework, that does not used for limiting the implementationtype of the moving object detection system 1. Engineers skilled inmoving object detection technologies should know that, both the imagefeature verification module and the object tracking module can also beestablished by using mathematical algorithms, so as to be provided in anexecution device such as processor, industrial computer, servercomputer, desk computer, laptop computer, tablet computer, smart phone,or smart watch by a form of application program, library, variables, oroperands. Accordingly, the present invention simultaneously provides amoving object detection method capable of being implemented in anexecution device like the controlling and processing device 12 shown inFIG. 3.

FIG. 8A and FIG. 8B show flowcharts of a moving object detection methodaccording to the present invention. In flow procedure of the movingobject detection method, with reference to FIG. 3 and FIG. 8Asimultaneously, step S1 is firstly executed for providing an imagecapturing device 11 for applying an image capturing process to amonitoring area having at least one object 3. Next, in step S2 of themethod flow, a controlling and processing module 12 comprising apre-processer module 121, a feature extraction module 122, an imageoptical flow estimation module 123, a feature points grouping module124, and a moving object determination module 125 is provided as theillustrations of FIG. 3 and FIG. 4, and then the pre-processer module121 of the controlling and processing module 12 is utilized to apply abackground subtraction process to a plurality of image frames receivedfrom the image capturing device 11. It needs to further explain that,the plurality of image frames comprises a plurality of image framepairs, and each of the plurality of image frame pairs consists of aprevious-time image frame and a current-time image frame. The saidprevious-time image frame and the said current-time image frame can berespectively an image frame captured at time point “t−1” and an imageframe captured at time point “t”, and can also be respectively an imageframe captured at time point “t” and an image frame captured at timepoint “t+1”.

Subsequently, the method flow proceeds to step S3 so as to apply afeature extraction process, by the feature extraction module 122, to theplurality of image frames after the background subtraction process iscompleted, such that a plurality of corner feature points are obtainedfrom each of the plurality of image frames. With reference to FIG. 5 andFIG. 9, the step S3 comprises three detail execution steps, wherein stepS31 is configured for providing a feature difference calculation unit1221, an matrix establishing unit 1222 and a corner feature pointacquiring unit 1223 in the feature extraction module 122, and then usingthe feature difference calculation unit 1221 to apply a patch-basedfeature difference calculating process to the plurality of image frames.Next, step S32 is executed, such that the matrix establishing unit 1222is adopted for establishing a plurality of structure tensor matricesbased on a plurality of feature difference calculation data provided bythe feature difference calculation unit 1221. In the end of theprocedure of the three detail execution steps, it proceeds to step S33for applying a feature value converting process, by the corner featurepoint acquiring unit 1223, to the plurality of structure tensor matricesfor obtaining the plurality of corner feature points. In the presentinvention, above-presented mathematic equations (1)-(4) andabove-mentioned specific corner feature points verifying approach areutilized to make the corner feature point acquiring unit 1223 quicklyverify and choose corner feature points from the image frames.

Please refer to FIG. 3, FIG. 4, FIG. 8A, and FIG. 8B again. The methodflow subsequently proceeds to step S4, so as to use the image opticalflow estimation module 123 to establish a plurality of image pyramidpairs based on the plurality of image frame pairs after the featureextraction process is completed, and subsequently to; wherein each ofthe plurality of image pyramid pairs comprises a previous-time imagepyramid and a current-time image pyramid. After that, step S5 isexecuted for applying an optical flow calculation process to theplurality of corner feature points contained in each of the plurality ofimage pyramid pairs by the image optical flow estimation module 123. AsFIG. 6 shows, the previous-time image is treated with two-times ofGaussian upsampling convolution processes, such that three levels ofimages constitute a previous-time image pyramid. Moreover, thecurrent-time image is applied with two-times of Gaussian downsamplingconvolution processes in order to generate a current-time image pyramidformed by three levels of images. After the image pyramid pairs aregenerated, the feature tracking unit 1232 in the image optical flowestimation module 123 is adopted for using Lucas-Kanade (LK) opticalflow approach to complete the optical flow calculation process of theprevious-time image pyramid and the current-time image pyramid. However,owing to the fact that the calculated optical flow value of each of theplurality of corner feature points may be nonlinear, linearinterpolation should be utilized for achieving a liner correction on theoptical flow value of each of the plurality of corner feature pointscontained in the image pyramid pairs.

After the steps S4-S5 are completed, the method flow is proceeded tostep S6, such that the feature points grouping module 124 is utilized toapply a cluster labeling process to the plurality of corner featurepoints contained in each of the plurality of image pyramid pairs, andthen a plurality of corner feature point groups are defined. Forinstance, a feature point (pixel value) in a patch would be verified asa corner feature point P in the case of the fact that R is greater thana pre-defined threshold value and is also a local maximum value in thepatch. Subsequently, the feature points grouping module 124 woulddetermine whether the number of feature points in a pre-defined radiusfrom the corner feature point P is smaller than a minimum thresholdvalue or not. If yes, those feature points are clustered to an identicalcorner feature point group by the feature points grouping module 124.After the plurality of corner feature point groups are obtained, themoving object determination module 125 is configured to achieve a motiondetection of the at least one object 3 by determining whether theplurality of corner feature point groups move or not.

Therefore, through above descriptions, the moving object detectionsystem and method provided by the present invention has been introducedcompletely and clearly; in summary, the present invention includes theadvantages of:

(1) The present invention discloses a moving object detection system andmethod. The system comprises an image capturing device 11 and acontrolling and processing module 12, wherein a pre-processer module121, a feature extraction module 122, an image optical flow estimationmodule 123, a feature points grouping module 124, and a moving objectdetermination module 125 are provided in the controlling and processingmodule 12 by a form of application program, library, variables, oroperands. In the present invention, more particularly, a featuredifference calculation unit 1221, a matrix establishing unit 1222 and acorner feature point acquiring unit 1223 are provided in the featureextraction module 122, and that is helpful for largely enhancing thecomputing speed of the controlling and processing device 12 in verifyingand choosing corner feature points from the image frames. Therefore,after the corner feature points are applied with a cluster labelingprocess, the moving object determination module 125 can achieve motiondetection of at least one object 3 locating in a monitoring area by justdetermining whether corner feature point groups move or not.

The above description is made on embodiments of the present invention.However, the embodiments are not intended to limit scope of the presentinvention, and all equivalent implementations or alterations within thespirit of the present invention still fall within the scope of thepresent invention.

What is claimed is:
 1. A moving object detection system, comprising: an image capturing device, being configured to apply an image capturing process to a monitoring area having at least one object, thereby generating a plurality of image frames with time consecutiveness; and a controlling and processing device, being configured to perform a plurality of functions consisting of: (a) applying a background subtraction process to the plurality of image frames; wherein the plurality of image frames comprises a plurality of image frame pairs, and each of the plurality of image frame pairs consisting of a previous-time image frame and a current-time image frame; (b) extracting a plurality of corner feature points from each of the plurality of image frames; (c) establishing a plurality of image pyramid pairs based on the plurality of image frame pairs and applying an optical flow calculation process to the plurality of corner feature points contained in each of the plurality of image pyramid pairs; (d) applying a cluster labeling process to the plurality of corner feature points contained in each of the plurality of image pyramid pairs, so as to obtain a plurality of corner feature point groups; and (e) achieving a motion detection of the at least one object by using a motion vector calculation algorithm to determine whether the plurality of corner feature point groups move or not.
 2. The moving object detection system of claim 1, wherein the controlling and processing device is selected from the group consisting of processor, industrial computer, server computer, desk computer, laptop computer, tablet computer, smart phone, and smart watch.
 3. The moving object detection system of claim 1, wherein the controlling and processing device performs the forgoing function (b) through steps of: (b1) applying a patch-based feature difference calculating process to the plurality of image frames by using a feature difference calculation algorithm that is selected from the group consisting of mean absolute differences (MAD) algorithm, sum of absolute differences (SAD) algorithm, sum of squared differences (SSD) algorithm, and scale-invariant feature transform (SIFT) algorithm, thereby generating a plurality of feature difference calculation data; (b2) establishing a plurality of structure tensor matrices based on the plurality of feature difference calculation data; and (b3) applying a feature value converting process to the plurality of structure tensor matrices, so as to obtain the plurality of corner feature points.
 4. The moving object detection system of claim 1, wherein the controlling and processing device performs the forgoing function (c) through steps of: (c1) applying at least two times of Gaussian convolution processes to each of the plurality of image frame pairs, so as to establish the plurality of image pyramid pairs; and (c2) using Lucas-Kanade optical flow approach to complete the optical flow calculation process of the previous-time image pyramid and the current-time image pyramid.
 5. The moving object detection system of claim 1, wherein the motion vector calculation algorithm is selected from the group consisting of least squares estimation algorithm, minimum least squares estimation algorithm, and damped least square estimation algorithm.
 6. A moving object detection method, comprising following steps: (1) providing an image capturing device for applying an image capturing process to a monitoring area having at least one object; (2) providing a controlling and processing device comprising a pre-processer module, a feature extraction module, an image optical flow estimation module, a feature points grouping module, and a moving object determination module , so as to use the pre-processer module of the controlling and processing device to apply a background subtraction process to a plurality of image frames received from the image capturing device; wherein the plurality of image frames comprises a plurality of image frame pairs, and each of the plurality of image frame pairs consisting of a previous-time image frame and a current-time image frame; (3) applying a feature extraction process, by the feature extraction module, to the plurality of image frames after the background subtraction process is completed, so as to obtain a plurality of corner feature points from each of the plurality of image frames; (4) using the image optical flow estimation module to establish a plurality of image pyramid pairs based on the plurality of image frame pairs after the feature extraction process is completed; wherein each of the plurality of image pyramid pairs comprises a previous-time image pyramid and a current-time image pyramid; (5) applying an optical flow calculation process to the plurality of corner feature points contained in each of the plurality of image pyramid pairs by the image optical flow estimation module; (6) applying a cluster labeling process, by the feature points grouping module, to the plurality of corner feature points contained in each of the plurality of image pyramid pairs, so as to obtain a plurality of corner feature point groups; and (7) the moving object determination module being configured to achieve a motion detection of the at least one object by determining whether the plurality of corner feature point groups move or not.
 7. The moving object detection method of claim 6, wherein the image capturing device is connected to a camera carrying device, and the camera carrying device being selected from the group consisting of aerial photography drone, unmanned aerial vehicle (UAV), robotics system for space exploration, robot, and robot cleaner.
 8. The moving object detection method of claim 6, wherein the controlling and processing device is selected from the group consisting of processor, industrial computer, server computer, desk computer, laptop computer, tablet computer, smart phone, and smart watch.
 9. The moving object detection method of claim 6, wherein the moving object determination module is provided with a motion vector calculation algorithm therein for achieving the motion detection of the at least one object, and the motion vector calculation algorithm being selected from the group consisting of least squares estimation algorithm, minimum least squares estimation algorithm, and damped least square estimation algorithm.
 10. The moving object detection method of claim 6, wherein the step (3) comprising following detail steps: (31) providing a feature difference calculation unit, an matrix establishing unit and a corner feature point acquiring unit in the feature extraction module, and subsequently using the feature difference calculation unit to apply a patch-based feature difference calculating process to the plurality of image frames; (32) using the matrix establishing unit to establish a plurality of structure tensor matrices based on a plurality of feature difference calculation data provided by the feature difference calculation unit; and (33) applying a feature value converting process, by the corner feature point acquiring unit, to the plurality of structure tensor matrices for obtaining the plurality of corner feature points.
 11. The moving object detection method of claim 10, wherein the feature difference calculation unit is provided with a feature difference calculation algorithm therein for achieving the patch-based feature difference calculating process, and the feature difference calculation algorithm being selected from the group consisting of mean absolute differences (MAD) algorithm, sum of absolute differences (SAD) algorithm, sum of squared differences (SSD) algorithm, and scale-invariant feature transform (SIFT) algorithm. 